SizeGAN: Improving Size Representation in Clothing Catalogs
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Abstract (arXiv)
Online clothing catalogs lack diversity in body shape and garment size. Brands commonly display their garments on models of one or two sizes, rarely including plus-size models. To our knowledge, our paper presents the first method for generating images of garments and models in a new target size to tackle the size under-representation problem. Our primary technical contribution is a conditional generative adversarial network that learns deformation fields at multiple resolutions to realistically change the size of models and garments. Results from our two user studies show SizeGAN outperforms alternative methods along three dimensions -- realism, garment faithfulness, and size -- which are all important for real world use.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 30 references that are in the map. First posted on arXiv 5 Nov 2022.
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23 of 30 references.
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